Plots - Edge angle analysis
Lisa Schunk
2022-11-03 12:44:15
1 Goal of the script
This script plots the data from the edge angle analysis (archaeology) in order to visualizes the measurements.
The plots will also visualize the differences between the three methods.
dir_in <- "analysis/derived_data/"
dir_out <- "analysis/plots"Raw data must be located in ~/analysis/derived_data/.
Formatted data will be saved in ~/analysis/plots. The knit directory for this script is the project directory.
2 Load packages
pack_to_load <- c("R.utils", "tools", "openxlsx", "ggplot2", "tidyverse", "patchwork", "doBy", "ggrepel")
sapply(pack_to_load, library, character.only = TRUE, logical.return = TRUE)Warning: package 'R.utils' was built under R version 4.1.3Warning: package 'R.oo' was built under R version 4.1.3Warning: package 'R.methodsS3' was built under R version 4.1.3Warning: package 'openxlsx' was built under R version 4.1.3Warning: package 'ggplot2' was built under R version 4.1.3Warning: package 'tidyverse' was built under R version 4.1.3Warning: package 'tibble' was built under R version 4.1.3Warning: package 'tidyr' was built under R version 4.1.3Warning: package 'readr' was built under R version 4.1.3Warning: package 'dplyr' was built under R version 4.1.3Warning: package 'stringr' was built under R version 4.1.3Warning: package 'forcats' was built under R version 4.1.3Warning: package 'patchwork' was built under R version 4.1.3Warning: package 'doBy' was built under R version 4.1.3 R.utils tools openxlsx ggplot2 tidyverse patchwork doBy ggrepel
TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE 3 Get name, path and information of the file
data_file <- list.files(dir_in, pattern = "\\.Rbin$", full.names = TRUE)
md5_in <- md5sum(data_file)
info_in <- data.frame(file = basename(names(md5_in)), checksum = md5_in, row.names = NULL)4 Load data into R object
imp_data <- loadObject(data_file)
str(imp_data)'data.frame': 58516 obs. of 11 variables:
$ Site : chr "Balve" "Balve" "Balve" "Balve" ...
$ ID : chr "MU-022" "MU-022" "MU-022" "MU-022" ...
$ Tool_type : chr "KM_dex" "KM_dex" "KM_dex" "KM_dex" ...
$ Section : num 1 1 1 1 1 1 1 1 1 1 ...
$ Edge : chr "E1" "E1" "E1" "E1" ...
$ Angle_number : int 1 2 3 4 5 6 7 8 9 10 ...
$ Distance_origin: num 1 2 3 4 5 6 7 8 9 10 ...
$ Segment : num 2 2 2 2 2 2 2 2 2 2 ...
$ Three_point : num 158.6 137.6 124 105.4 89.5 ...
$ Two_lines : num 137.9 106.4 71.6 41.4 35.9 ...
$ Best_fit : num 138.8 107.7 70.5 42.2 30.8 ...The imported file is: “~/analysis/derived_data/EdgeAngle.Rbin”
5 Define numeric variables
num.var <- 9:length(imp_data)6 Plot edge angle measurements
6.1 Plots showing the angles as lines
# split the data in the individual 227 samples
sp <- split(imp_data, imp_data[["ID"]])
for (j in seq_along(sp)) {
p <- vector(mode = "list", length = length(num.var))
names(p) <- names(sp[[j]])[num.var]
for (i in seq_along(num.var)){
p[[i]] <- ggplot(data = sp[[j]], aes_string(y = "Section",
x = names(sp[[j]])[num.var[i]], colour = "Angle_number")) +
geom_line(aes(group = Angle_number), orientation = "y") +
geom_hline(yintercept = c(2, 9), linetype = "dashed") +
facet_wrap(~ Edge) +
scale_colour_continuous(trans = "reverse") +
scale_y_continuous(breaks=1:10, trans = "reverse") +
labs(y = "Section") +
labs(colour = "Dis. origin (mm)") +
labs(x = gsub("_", " ", names(sp[[j]])[num.var[i]])) +
scale_x_continuous(n.breaks = 3) +
theme_classic()
}
wp <- wrap_plots(p) + plot_annotation(title = names(sp)[j]) +
plot_layout(guides = "collect")
print(wp)
# save to PDF
file_out <- paste0(file_path_sans_ext(info_in[["file"]]), "_EA_plot_", names(sp)[j],
".pdf")
ggsave(filename = file_out, plot = wp, path = dir_out,
device = "pdf", width = 260, height = 170, units = "mm")
} 
The files will be saved as “~/analysis/plots.[ext]”.
7 sessionInfo() and RStudio version
sessionInfo()R version 4.1.1 (2021-08-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)
Matrix products: default
locale:
[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252
[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C
[5] LC_TIME=German_Germany.1252
attached base packages:
[1] tools stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] ggrepel_0.9.1 doBy_4.6.13 patchwork_1.1.2 forcats_0.5.2
[5] stringr_1.4.1 dplyr_1.0.10 purrr_0.3.4 readr_2.1.2
[9] tidyr_1.2.1 tibble_3.1.8 tidyverse_1.3.2 ggplot2_3.3.6
[13] openxlsx_4.2.5 R.utils_2.12.0 R.oo_1.25.0 R.methodsS3_1.8.2
loaded via a namespace (and not attached):
[1] httr_1.4.4 sass_0.4.2 jsonlite_1.8.0
[4] modelr_0.1.9 bslib_0.4.0 microbenchmark_1.4.9
[7] assertthat_0.2.1 highr_0.9 googlesheets4_1.0.1
[10] cellranger_1.1.0 yaml_2.3.5 pillar_1.8.1
[13] backports_1.4.1 lattice_0.20-44 glue_1.6.2
[16] digest_0.6.29 rvest_1.0.3 colorspace_2.0-3
[19] htmltools_0.5.3 Matrix_1.5-1 pkgconfig_2.0.3
[22] broom_1.0.1 haven_2.5.1 scales_1.2.1
[25] tzdb_0.3.0 googledrive_2.0.0 farver_2.1.1
[28] generics_0.1.3 ellipsis_0.3.2 cachem_1.0.6
[31] withr_2.5.0 cli_3.4.0 magrittr_2.0.3
[34] crayon_1.5.1 readxl_1.4.1 evaluate_0.16
[37] fs_1.5.2 fansi_1.0.3 MASS_7.3-54
[40] xml2_1.3.3 hms_1.1.2 gargle_1.2.1
[43] lifecycle_1.0.2 munsell_0.5.0 reprex_2.0.2
[46] zip_2.2.1 compiler_4.1.1 Deriv_4.1.3
[49] jquerylib_0.1.4 rlang_1.0.5 grid_4.1.1
[52] rstudioapi_0.14 labeling_0.4.2 rmarkdown_2.16
[55] gtable_0.3.1 DBI_1.1.3 R6_2.5.1
[58] lubridate_1.8.0 knitr_1.40 fastmap_1.1.0
[61] utf8_1.2.2 stringi_1.7.8 Rcpp_1.0.9
[64] vctrs_0.4.1 dbplyr_2.2.1 tidyselect_1.1.2
[67] xfun_0.33 RStudio version 1.3.1073.
8 Cite R packages used
for (i in pack_to_load) print(citation(i), bibtex = FALSE)
To cite package 'R.utils' in publications use:
Henrik Bengtsson (2022). R.utils: Various Programming Utilities. R
package version 2.12.0. https://CRAN.R-project.org/package=R.utils
The 'tools' package is part of R. To cite R in publications use:
R Core Team (2021). R: A language and environment for statistical
computing. R Foundation for Statistical Computing, Vienna, Austria.
URL https://www.R-project.org/.
We have invested a lot of time and effort in creating R, please cite it
when using it for data analysis. See also 'citation("pkgname")' for
citing R packages.
To cite package 'openxlsx' in publications use:
Philipp Schauberger and Alexander Walker (2021). openxlsx: Read,
Write and Edit xlsx Files. R package version 4.2.5.
https://CRAN.R-project.org/package=openxlsx
To cite ggplot2 in publications, please use:
H. Wickham. ggplot2: Elegant Graphics for Data Analysis.
Springer-Verlag New York, 2016.
Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R,
Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E,
Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi
K, Vaughan D, Wilke C, Woo K, Yutani H (2019). "Welcome to the
tidyverse." _Journal of Open Source Software_, *4*(43), 1686. doi:
10.21105/joss.01686 (URL: https://doi.org/10.21105/joss.01686).
To cite package 'patchwork' in publications use:
Thomas Lin Pedersen (2022). patchwork: The Composer of Plots. R
package version 1.1.2. https://CRAN.R-project.org/package=patchwork
To cite package 'doBy' in publications use:
Søren Højsgaard and Ulrich Halekoh (2022). doBy: Groupwise
Statistics, LSmeans, Linear Estimates, Utilities. R package version
4.6.13. https://CRAN.R-project.org/package=doBy
ATTENTION: This citation information has been auto-generated from the
package DESCRIPTION file and may need manual editing, see
'help("citation")'.
To cite package 'ggrepel' in publications use:
Kamil Slowikowski (2021). ggrepel: Automatically Position
Non-Overlapping Text Labels with 'ggplot2'. R package version 0.9.1.
https://CRAN.R-project.org/package=ggrepelEND OF SCRIPT